No abstract

Recently, the severity of injuries resulting from traffic crashes has been extensively investigated in numerous studies. However, the number of studies that addressed the severity of the run-off-road (ROR) crashes is relatively low. In the Emirate of Abu Dhabi (AD), approximately 22% of the total serious crashes and fatalities that occurred from 2007 to 2013 were ROR crashes. Despite these facts and the uniqueness of the composition of licensed drivers in AD (approximately 87% of them are non-Emiratis), the factors affecting the occurrence and severity of ROR crashes in AD have not been explicitly addressed in any prior studies. Therefore, this study aims to investigate the characteristics of at-fault drivers involved in ROR crashes in AD, the nature and main causes of those crashes. In this regard, conditional distribution and two-way contingency tables were developed. In addition, this study aims to identify and quantify the factors affecting the severity of ROR crashes such as driver, road, vehicle and environment factors. To achieve this goal, ordered probit model approach was employed. Crash data for a total of 3819 ROR crashes that occurred in AD were employed in the analysis. The results indicated that driver factors (carelessness, speeding, and nationality), vehicle characteristics (vehicle type), and road and environment factors (road type, crash location and road surface condition) were the significant factors influencing the severity of ROR crashes in AD. Countermeasures to improve traffic safety and reduce numbers and severity of ROR crashes in AD were discussed.

The main objective of this research project is to develop, implement and test an efficient real-time system for the allocation of patrol cars to various locations within the boundaries of the Abu Dhabi Emirate. Patrol cars are allocated initially to provide adequate coverage over a wide area that is divided into geographic zones. However, during daily operations the patrol cars move to deal with traffic accidents or calls for assistance originating from various locations in each zone. The proposed patrol allocation system is essentially an automated decision support system that relies on Geographic Information System (GIS) and other data related to current and real-time traffic flow to assist the dispatch operators in making effective patrol allocation decisions. The system also computes the number of patrol vehicles required in order to make sure that the response time falls below a given threshold or upper limit. The basic system concept and structure are presented.

Based on historical evidence, driving in heavy fog conditions is one of the most serious causes that lead to massive highway accidents. For example, the Abu Dhabi-Dubai Highway (E10) faced two record accidents in recent times. The first accident was in March 2008 in which more than 200 vehicles were involved in a mass collision. The second was in April 2011 and it involved 130 vehicles. These two massive accidents, and several other relatively minor ones, were due to poor visibility conditions caused by dense fog. Vehicles driving at high speed suddenly enter road sectors covered by dense fog without warning and are then implicated in mass collisions. The main objective of this research is to improve road safety in Abu Dhabi when drivers face poor visibility conditions caused by dense fog. This is achieved by sending early real-time warning signals to all drivers who are about to enter poor visibility sections of the highway of the dangers ahead, using radio signals or cell phone voice-based short messages. Warning signals can also be displayed to the drivers using Changeable Message Signs (CMS) installed along the highway. The concept and architecture of a novel, modular fog warning system are presented. 

Traffic crashes involving heavy trucks long have been a major concern in the field of traffic safety because of their great effect on accident severity. The emirate of Abu Dhabi, capital of the United Arab Emirates, features a unique situation: several roads designed mainly for truck movement. Even though those roads were constructed more than 10 years ago to decrease the severity of truck-related crashes, no prior studies have examined their effects on traffic safety improvements. The goals of this study were to understand better the nature, characteristics, and causes of heavy truck crashes occurring in Abu Dhabi; to identify the factors associated with crash severities; and to examine the probability of truck crashes involving fatalities on truck roads versus on mixed-vehicle roads. Data were analyzed from a sample of 1,426 heavy truck–related crashes with reported fatalities or injuries that occurred in Abu Dhabi between 2007 and 2013. First, conditional distributions, two-way analysis, and odds ratios were performed. Second, ordered probit and structural equation models were developed. Results indicated that the likelihood of truck crashes involving fatalities was 35% higher on truck roads than on mixed-vehicle roads. In addition, findings showed that human error, driver education, location, road type, and road speed variables were significant in affecting the severity of heavy truck– related crashes. Finally, practical suggestions on how to reduce the number of heavy truck–related crashes in Abu Dhabi are presented and discussed.